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Open access

Statistical foundations of actuarial learning and its applications / Mario V. Wüthrich, Michael Merz.

9783031124099 (electronic bk.)
303112409X (electronic bk.)
9783031124082
3031124111
9783031124112

Cham : Springer, [2023]

©2023

English

English.

1 online resource (xii, 605 pages) : illustrations.

10.1007/978-3-031-12409-9 doi

HG8781

368/.01

This open access book discusses the statistical modeling of insurance problems, a process which comprises data collection, data analysis and statistical model building to forecast insured events that may happen in the future. It presents the mathematical foundations behind these fundamental statistical concepts and how they can be applied in daily actuarial practice. Statistical modeling has a wide range of applications, and, depending on the application, the theoretical aspects may be weighted differently: here the main focus is on prediction rather than explanation. Starting with a presentation of state-of-the-art actuarial models, such as generalized linear models, the book then dives into modern machine learning tools such as neural networks and text recognition to improve predictive modeling with complex features. Providing practitioners with detailed guidance on how to apply machine learning methods to real-world data sets, and how to interpret the results without losing sight of the mathematical assumptions on which these methods are based, the book can serve as a modern basis for an actuarial education syllabus.

Includes bibliographical references and index.

Open access

Online resource; title from PDF title page (SpringerLink, viewed December 20, 2022).

Springer actuarial. 2523-3270

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